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Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage.

, , , , , and . CoNEXT, page 180-186. ACM, (2017)

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Investigating the mobile phone data to estimate the origin destination flow and analysis; case study: Paris region, , , , and . Transportation Research Procedia, (2015)Characterizing Mobile Service Demands at Indoor Cellular Networks., , , , , , , and . IMC, page 645-659. ACM, (2023)Characterizing and Modeling Session-Level Mobile Traffic Demands from Large-Scale Measurements., , , and . IMC, page 696-709. ACM, (2023)AppShot: A Conditional Deep Generative Model for Synthesizing Service-Level Mobile Traffic Snapshots at City Scale., , , , , and . IEEE Trans. Netw. Serv. Manag., 19 (4): 4136-4150 (December 2022)Characterizing 5G Adoption and its Impact on Network Traffic and Mobile Service Consumption., , , , , and . INFOCOM, page 1531-1540. IEEE, (2024)Microscope: mobile service traffic decomposition for network slicing as a service., , , and . MobiCom, page 38:1-38:14. ACM, (2020)Does Showing Off Help to Make Friends? Experimenting a Sociological Game on Self-Exhibition and Social Networks., , , , , , , , , and . ICWSM, The AAAI Press, (2009)Extracting Semantics of Individual Places from Movement Data by Analyzing Temporal Patterns of Visits., , , , , and . COMP@SIGSPATIAL, page 9-15. ACM, (2013)Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage., , , , , and . CoNEXT, page 180-186. ACM, (2017)SpectraGAN: spectrum based generation of city scale spatiotemporal mobile network traffic data., , , , , , , and . CoNEXT, page 243-258. ACM, (2021)